class_docstr: Post-processing methods for ABCNet.
  
  Args:
      num_classes (int): Number of classes.
      use_sigmoid_cls (bool): Whether to use sigmoid for classification.
      strides (tuple): Strides of each feature map.
      norm_by_strides (bool): Whether to normalize the regression targets by
          the strides.
      bbox_coder (dict): Config dict for bbox coder.
      text_repr_type (str): Text representation type, 'poly' or 'quad'.
      with_bezier (bool): Whether to use bezier curve for text detection.
      train_cfg (dict): Config dict for training.
      test_cfg (dict): Config dict for testing.
code: def merge_predict(self, spotting_data_samples, recog_data_samples):
      texts = [ds.pred_text.item for ds in recog_data_samples]
      start = 0
      for spotting_data_sample in spotting_data_samples:
          end = start + len(spotting_data_sample.pred_instances)
          spotting_data_sample.pred_instances.texts = texts[start:end]
          start = end
      return spotting_data_samples
  
file_path: mmocr/projects/ABCNet/abcnet/model/abcnet_postprocessor.py
imports: from mmocr.models.textdet.postprocessors.base import BaseTextDetPostProcessor
  from mmocr.registry import MODELS
  from ..utils import bezier2poly
label_desc: Use image node_id 2 for calling the ABCNetPostprocessor obj's underlying member method code with example usage: obj.merge_predict(spotting_data_samples, recog_data_samples) and returns: spotting_data_samples